1 Learning Outcomes
2 The usmap package
3 Get and Clean COVID Data
4 usmap Package
5 Add data to Maps
- 5.1 State Level Data
- 5.2 County Level Data

1 Learning Outcomes

Create Static Choropleth Maps of US States and Counties in R
References: usmap Package Vingette

2 The usmap package

The package allows you to obtain United States map data frames of varying region types (e.g. county, state. multi-state regions).
The map data frames include Alaska and Hawaii conveniently placed to the bottom left, as they appear in most maps of the US.
Convenience functions for plotting choropleths and working with FIPS codes are also provided.
The nice thing about usmap::plot_usmap() is it returns a ggplot object, which means we can add ggplot layers to the plot right out of the box.
```
library(tidyverse)
library(lubridate)
library(usmap)
data(statepop)
```
2 Load State Data

From Census Bureau

states <- read_csv("./data/state_geo_data.csv", col_types = cols(OID = col_character()))

3 Get and Clean COVID Data

3.1 Get Data

Updated Daily

 url_in <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/"
df <- tibble(file_names = c("time_series_covid19_confirmed_US.csv",
                    "time_series_covid19_deaths_US.csv"))
df %>% 
  mutate(url = str_c(url_in, file_names, sep =""),
         data = map(url, ~read_csv(., col_types = cols(FIPS = col_factor()), na = "")),
         case_type = as.factor(str_extract(file_names,"[:alpha:]*_[gU][:alpha:]*"))) %>%
  select(case_type, data) ->
  df

 df$vars <- map(df$data,  names)

3.2 Clean Data

Fix names and create consistent variables

 #create a helper function
    fix_names <- function(df, pattern, repl){
     stopifnot(is.data.frame(df), is.character(pattern), is.character(repl))
      names(df) <- str_replace_all(names(df), pattern, repl)
      return(df)
    }

df %>% 
  mutate(data = map(data, ~ fix_names(., "Admin2", "County")),
         data = map(data, ~ fix_names(., "Long_", "Long")),
         data = map_if(data, !str_detect(df$case_type,"deaths_US"),
                       ~ mutate(., Population = 0)),
         data = map(data, ~filter(., !is.na(FIPS))),
         data = map(data, ~ mutate(., cFIPS = str_extract(UID,".{5}$"),
                                   sFIPS = str_extract(cFIPS, "^..")))
         )->df

df$vars <- map(df$data,  names)

3.3 Tidy Data

Reshape and Fix Date Class

df %>% 
  mutate(data = map(data, ~ pivot_longer(data = ., cols = contains("/"), 
                                         names_to = "Date", 
                                         values_to = "Daily_Total") ))-> 
df

#helper function for Date Change
fix_date_mdy <- function(df,var_name){
  stopifnot(is.data.frame(df), is.character(var_name))
  df[[var_name]]<- mdy(df[[var_name]])
  return(df)
}

df %>%
  mutate(data = map(data, ~fix_date_mdy(., "Date")))-> 
df

3.4 Unnest into Data Frame

Save as new df and remove the old df and vars

   df %>% 
  unnest(cols=data) %>% 
  ungroup() ->
  df_all
   # rm(df)
df_all <- select(df_all, -vars)

3.5 Add Variables

Fix Population and Daily Totals

df_all %>% 
  group_by(UID) %>% 
  filter(!is.na(cFIPS), County !="Unassigned", !str_detect(County, "^Out of ")) %>% 
  mutate(Population = max(Population),
         Daily_Total_percap = Daily_Total/Population)->
  df_all
#write_csv(df_all, "./data/covid_us_data.csv")

4 usmap Package

4.1 Create a map of the states

Empty map

plot_usmap(regions = "states") + 
  labs(title = "US States",
       subtitle = "This is a blank map of the states of the United States.") + 
  theme(panel.background = element_rect(color = "black", fill = "lightblue"))

4.2 Create a map of the South Atlantic region

Predefined Census Regions

plot_usmap(include = .south_atlantic, labels = TRUE) + 
  labs(title = "US South Atlantic States",
       subtitle = "This is a blank map of the states in the South Atlantic Census Region") + 
  theme(panel.background = element_rect(color = "black", fill = "lightblue"))

4.3 Create a map of the Counties

Empty Map

plot_usmap(regions = "counties") + 
  labs(title = "US Counties",
       subtitle = "This is a blank map of the counties of the United States.") + 
  theme(panel.background = element_rect(color = "black", fill = "lightblue"))

4.4 Create a map of the Counties in Specific States

include= States by US abbreviations

plot_usmap("counties", include = c("VA", "MD", "DC")) +
  labs(title = "Custom DMV Area",
       subtitle = "These are the counties in MD, DC< anD VA.")+ 
  theme(panel.background = element_rect(color = "blue", fill = "lightblue"))

4.5 Create a map of the Specific Counties

Select by FIPS (five digit code)

dmv_county_fips <- c("11001","24003","24009","24017","24021","24027","24031",
                     "24033","51013","51059","51061","51107","51153","51179",
                     "51510","51600","51610","51630","51683","51685")
plot_usmap("counties", include = dmv_county_fips) +
  labs(title = "Greater DMV Area",
       subtitle = "These are the Counties/Cities in the Greater DMV area.")+ 
  theme(panel.background = element_rect(color = "blue", fill = "lightblue"))

5 Add data to Maps

5.1 State Level Data

Current Totals Cases by State

df_all %>% 
  filter(Date == max(Date), Population >0, !is.na(sFIPS)) %>% 
  group_by(sFIPS, case_type, Date) %>% 
  summarise(Current_Total = sum(Daily_Total),
            Current_Total_percap = sum(Daily_Total)/sum(Population))->
  state_totals

Add to US Maps

state_totals %>% 
  filter(case_type =="confirmed_US") %>% 
  rename(fips = sFIPS)-> 
  state_cases
plot_usmap(data = state_cases, values = "Current_Total", color = "blue") + 
  scale_fill_continuous(low = "white", high = "red", 
                        name = "Confirmed Cases", label = scales::comma)+
  labs(title = "US States",
       subtitle = paste0("Total Cases by State as of ", 
                         max(state_cases$Date))) + 
  theme(panel.background = element_rect(color = "black", fill = "white")) +
  theme(legend.position = "top")

state_totals %>% 
  filter(case_type =="deaths_US") %>% 
  rename(fips = sFIPS)-> 
  state_cases
plot_usmap(data = state_cases, values = "Current_Total", color = "blue") + 
  scale_fill_continuous(low = "white", high = "red", 
                        name = "Total Deaths", label = scales::comma)+
  labs(title = "US States",
       subtitle = paste0("Total Deaths by State as of ", max(state_cases$Date))) + 
  theme(panel.background = element_rect(color = "black", fill = "white")) +
  theme(legend.position = "top")

Per Capita Maps

state_totals %>% 
  filter(case_type =="confirmed_US") %>% 
  rename(fips = sFIPS)-> 
  state_cases
plot_usmap(data = state_cases, values = "Current_Total_percap", 
           color = "blue") + 
  scale_fill_continuous(low = "white", high = "red", 
                        name = "Confirmed Cases per Capita", 
                        label = scales::comma)+
  labs(title = "US States",
       subtitle = paste0("Total Cases per Capita by State as of ",
                         max(state_cases$Date))) + 
  theme(panel.background = element_rect(color = "black", fill = "white")) +
  theme(legend.position = "right")

state_totals %>% 
  filter(case_type =="deaths_US") %>% 
  rename(fips = sFIPS)-> 
  state_cases
plot_usmap(data = state_cases, values = "Current_Total_percap", 
           color = "blue") + 
  scale_fill_continuous(low = "white", high = "red", 
                        name = "Total Deaths Per Capita", 
                        label = scales::comma)+
  labs(title = "US States",
       subtitle = paste0("Total Deaths per Capita by State as of ", 
                         ... = max(state_cases$Date))) + 
  theme(panel.background = element_rect(color = "black", fill = "white")) +
  theme(legend.position = "right")

Using a log Scale

state_totals %>% 
  filter(case_type =="deaths_US") %>% 
  mutate(Current_Total10 = log10(Current_Total),
         Current_Total_percap10 = log10(Current_Total_percap)) %>% 
  rename(fips = sFIPS)-> 
  state_cases
plot_usmap(data = state_cases, values = "Current_Total_percap10", 
           color = "blue") + 
  scale_fill_continuous(low = "white", high = "red", 
                        name = "Total Deaths per Capita (Log 10)", 
                        label = scales::comma)+
  labs(title = "US States",
       subtitle = paste0("Log(10) of Total Deaths per Capita by State as of ", 
                         ... = max(state_cases$Date))) + 
  theme(panel.background = element_rect(color = "black", fill = "white")) +
  theme(legend.position = "right")

Per Area Maps

state_cases %>% 
  left_join(states, by = c("fips"="STATE")) %>% 
  mutate(Current_Total_perarea = Current_Total/AREALAND)->
  state_casesa

plot_usmap(data = state_casesa, values = "Current_Total_perarea", 
           color = "blue") + 
  scale_fill_continuous(low = "white", high = "red", 
                        name = "Total Deaths per unit Area (Log 10)", 
                        label = scales::comma)+
  labs(title = "US States",
       subtitle = paste0("Total Deaths per unit area by State as of ", 
                         ... = max(state_cases$Date))) + 
  theme(panel.background = element_rect(color = "black", fill = "white")) +
  theme(legend.position = "right")

5.2 County Level Data

Current Totals Cases by County

df_all %>% 
  filter(Date == max(Date), Population >0, !is.na(cFIPS)) %>% 
  group_by(cFIPS, case_type, Date) %>% 
  summarise(Current_Total = sum(Daily_Total),
            Current_Total_percap = sum(Daily_Total)/sum(Population))->
  county_totals

Add to a County Map for New York and New Jersey

county_totals %>% 
  filter(case_type =="confirmed_US") %>% 
  mutate(Current_Total_log2 = log2(Current_Total)) %>% 
  rename(fips = cFIPS)-> 
  county_cases
plot_usmap(data = county_cases, include = c("NY","NJ"),
           values = "Current_Total", color = "blue") + 
  scale_fill_continuous(low = "white", high = "red", 
                        name = "Confirmed Cases", label = scales::comma)+
  labs(title = "US States",
       subtitle = paste0("Total Cases by County as of ", 
                         max(state_cases$Date))) + 
  theme(panel.background = element_rect(color = "black", fill = "white")) +
  theme(legend.position = "top")

Add to a County/City Map for DMV

plot_usmap(data = county_cases, include = dmv_county_fips,
           values = "Current_Total", color = "blue") + 
  scale_fill_continuous(low = "white", high = "red", 
                        name = "Confirmed Cases", label = scales::comma)+
  labs(title = "DMV Region",
       subtitle = paste0("Total Cases by County/City as of ", 
                         max(state_cases$Date))) + 
  theme(panel.background = element_rect(color = "black", fill = "white")) +
  theme(legend.position = "top")

county_totals %>% 
  filter(case_type =="deaths_US") %>% 
  rename(fips = cFIPS)-> 
  county_deaths
plot_usmap(data = county_deaths, include = dmv_county_fips,
           values = "Current_Total", color = "blue") + 
  scale_fill_continuous(low = "white", high = "red", 
                        name = "Confirmed Deaths", label = scales::comma)+
  labs(title = "DMV Region",
       subtitle = paste0("Total Deaths by County/City as of ", 
                         max(state_cases$Date))) + 
  theme(panel.background = element_rect(color = "black", fill = "white")) +
  theme(legend.position = "top")

Per Capita Cases

plot_usmap(data = county_cases, include = dmv_county_fips,
           values = "Current_Total_percap", color = "blue") + 
  scale_fill_continuous(low = "white", high = "red", 
                        name = "Confirmed Cases Per Capita", label = scales::comma)+
  labs(title = "DMV Region",
       subtitle = paste0("Total Cases per Capita by County/City as of ", 
                         max(state_cases$Date))) + 
  theme(panel.background = element_rect(color = "black", fill = "white")) +
  theme(legend.position = "top")

Per Capita Deaths in DMV

county_totals %>% 
  filter(case_type =="deaths_US") %>% 
  rename(fips = cFIPS)-> 
  county_deaths
plot_usmap(data = county_deaths, include = dmv_county_fips,
           values = "Current_Total_percap", color = "blue") + 
  scale_fill_continuous(low = "white", high = "red", 
                        name = "Confirmed Deaths per Capita", label = scales::comma)+
  labs(title = "DMV Region",
       subtitle = paste0("Total Deaths per Capita by County/City as of ", 
                         max(state_cases$Date))) + 
  theme(panel.background = element_rect(color = "black", fill = "white")) +
  theme(legend.position = "top")

Mapping the US in R with the usmap Package

Richard Ressler

2020-04-17